It is well known that moving a video camera while it is capturing an image distorts the captured image. For example, “motion blur” appears. This is caused by the fact that to measure the luminous intensity of a point of a scene each pixel must continue to be exposed to the light emitted by that point for an exposure time te. If the video camera is moved during this time te, the pixel is not exposed to light from a single point but to that emitted by a plurality of points. The luminous intensity measured by this pixel is then that from a plurality of points of light, which causes motion blur to appear.
Nowadays, there also exist increasing numbers of rolling shutter video cameras. In those video cameras, the rows of pixels are captured one after the other, so that, in the same image, the moment of capturing one row of pixels is offset temporally by a time tΔ from the moment of capturing the next row of pixels. If the video camera moves during the time tΔ, that creates distortion of the captured image even if the exposure time te is considered negligible.
To correct such distortion, it is necessary to estimate correctly the speed of the video camera at the moment at which it captures the image.
To this end, methods known to the inventors for estimating the speed of movement of a first video camera at the moment when that first video camera is capturing a current image of a three-dimensional scene have been developed. These known methods are called feature-based methods. These feature-based methods include steps of extracting particular points in each image known as features. The features extracted from the reference image and the current image must then be matched. These steps of extracting and matching features are badly conditioned, affected by noise and not robust. They are therefore complex to implement.
The speed of movement of the first video camera is estimated afterwards on the basis of the speed of movement of these features from one image to another. However, it is desirable to simplify the known methods.